The present disclosure relates generally to treatment planning for radiation therapy and more particularly to treatment planning systems and methods that use multicriteria optimization and a progressive optimization scheme to develop treatment plans for volumetric modulated arc therapy (VMAT).
In general, radiation therapy consists of the use of ionizing radiation to treat living tissue, usually tumors. Many different types of ionizing radiation are used in radiation therapy, including high-energy x-rays, electron beams, and proton beams. The process of administering radiation therapy to a patient can be similar across different types of radiation. Typically, an external-beam radiation treatment system is used. Such systems provide a linear accelerator that produces a beam of the desired type at a beam source and collimators including a multileaf collimator (MLC) to shape the beam that emerges from the beam source. The beam delivery system (including the beam source and collimators) is generally mounted on a movable gantry that can be moved around a treatment couch on which a patient is placed, allowing the radiation beam to be delivered from different angles relative to the patient.
Systems of this kind are used for various treatment options. One option is intensity-modulated radiotherapy (IMRT), in which the beam source is positioned at a desired angle, and the MLC is modulated to control the dose received by different tissues. During a treatment session, the beam source and/or the MLC may be repositioned, allowing radiation to be delivered from different angles. In IMRT, the beam source remains stationary while radiation is being delivered. Another treatment option is volumetric modulated arc therapy (VMAT), in which the beam source traverses an arc around the patient while delivering radiation. In both IMRT and VMAT, the overarching goal is to deliver a therapeutically effective dose of radiation (typically a high and uniform dose) to a target volume (typically a tumor) within the patient's body while minimizing the dose delivered to surrounding tissues (in particular, healthy organs that may be located close to the target volume).
Effective radiation therapy requires treatment planning to determine machine parameters that will optimally achieve the overarching goal. In the case of IMRT, a treatment plan may specify machine parameters such as positions of the beam source and collimators (including MLC leaf settings), beam intensity (e.g., dose rate), and duration of exposure (also referred to as “beam-on time”); the plan may include multiple control points, each defined by a set of machine parameters. In the case of VMAT, a treatment plan may specify all of the same machine parameters as in IMRT, plus additional parameters defining an arc to be traversed and in some instances speed of traversing the arc. During treatment, a treatment plan can be used to control operation of the radiotherapy system, and operating the radiotherapy system according to the treatment plan results in delivering a desired dose distribution to the patient.
Treatment planning is usually approached via the “inverse” problem of determining the optimal combination of machine parameters—such as beam intensity, beam shaping, beam direction(s), exposure duration—to deliver a desired total radiation dose to the target volume (or multiple target volumes) while minimizing the dose delivered to nearby organs (sometimes referred to as “organs at risk,” or “OAR”). Given the many degrees of freedom, the inverse problem is generally not amenable to an analytic solution.
In the case of IMRT, interactive tools have been developed to facilitate finding a solution to the inverse problem. Traditionally, such tools are designed to find a single optimal solution by minimizing the value of an objective function. To formulate an objective function for an IMRT optimization problem, a desired outcome is first defined as a vector in a multidimensional space. A set of alternative solutions (which may be an infinite set) is defined, where each alternative solution has an associated alternative outcome, also defined as a vector in the multidimensional space. A cost function is defined to quantify a distance (in the multidimensional space) between any given alternative outcome and the desired outcome. Euclidean or other distance metrics can be used, and different components of the outcome vector may be assigned different weights in the cost function. The optimum solution can be identified by finding the alternative solution that minimizes the cost function.
For purposes of applying cost-function-based optimization processes to radiation therapy treatment planning, the outcome vector may be defined as a dose distribution that includes doses for at least one target volume and some number of non-target volumes for which low dose is optimal and no dose is ideal but generally not achievable. It is not immediately apparent how the different objectives should be weighted, and accordingly it may be desirable to allow the user to explore the effects of different weightings, a procedure sometimes referred to as multicriteria optimization.
Interactive tools exist to facilitate such exploration in the context of IMRT. Such tools receive a set of treatment objectives (e.g., identifying some number of volumes of interest and a desired dose for each such volume). Based on the treatment objectives, a library (e.g., database) of alternative plans is generated, e.g., by creating a Pareto-optimal IMRT plan for each treatment objective in turn, with each alternative plan having an associated dose matrix indicating the dose in each volume specified in the treatment objectives. The user (e.g., a radiation oncologist or other medical professional) interacts with the library through a graphical user interface to explore a navigation space defined by the alternative plans. Visualization tools allow the user to define an interpolation among the plans in the library; for instance, the user can operate control elements (e.g., onscreen sliders) to adjust the interpolation parameters and observe the effect on dose distribution. Once the user has achieved a desired dose distribution by adjusting the interpolation parameters, a final treatment plan is generated by an interpolation of database plans using the user-adjusted parameters. Such interfaces can provide a real-time, intuitive technique to facilitate treatment planning for IMRT.